Low latency intra-frame motion estimation based on clusters of ladar pulses

ABSTRACT

A ladar system can estimate intra-frame motion for an object within a field of view of the ladar system using a tight cluster of ladar pulses. For example, ladar pulses in a cluster can be spaced apart but overlapping with at least one of the other ladar pulses in that cluster at a specified distance in the field of view. A ladar receiver can then process the reflections from the cluster to computer intra-frame motion data, such as intra-frame velocity and intra-frame acceleration for an object.

CROSS-REFERENCE AND PRIORITY CLAIM TO RELATED PATENT APPLICATIONS

This patent application claims priority to U.S. provisional patentapplication 62/693,078, filed Jul. 2, 2018, and entitled “IntelligentLadar System with Low Latency Motion Planning Updates”, the entiredisclosure of which is incorporated herein by reference.

This patent application also claims priority to U.S. provisional patentapplication 62/558,937, filed Sep. 15, 2017, and entitled “IntelligentLadar System with Low Latency Motion Planning Updates”, the entiredisclosure of which is incorporated herein by reference.

This patent application is also related to (1) U.S. patent applicationSer. No. 16/106,350, filed this same day, and entitled “IntelligentLadar System with Low Latency Motion Planning Updates”, (2) U.S. patentapplication Ser. No. 16/106,374, filed this same day, and entitled“Ladar Receiver with Co-Bore Sited Camera”, and (3) U.S. patentapplication Ser. No. 16/106,441, filed this same day, and entitled“Ladar System with Intelligent Selection of Shot List Frames Based onField of View Data”, the entire disclosures of each of which areincorporated herein by reference.

INTRODUCTION

Safe autonomy in vehicles, whether airborne, ground, or sea-based,relies on rapid precision, characterization of, and rapid response to,dynamic obstacles. A conventional approach to autonomous obstacledetection and motion planning for moving vehicles is shown by FIG. 1.The system 100 for use with a vehicle comprises a motion planning system102 in combination with a suite 104 of sensors 106. The sensors 106 inthe suite 104 provide the motion planning system 102 with sensor data120 for use in the obstacle detection and motion planning process.Sensor data ingest interface 108 within the motion planning system 102receives the sensor data 120 from the sensors 106 and stores the sensordata 120 in a sensor data repository 130 where it will await processing.Motion planning intelligence 110 within the motion planning system 102issues read or query commands 124 to the sensor data repository 130 andreceives the requested sensor data as responses 126 to the queries 124.The intelligence 110 then analyzes this retrieved sensor data to makedecisions 128 about vehicle motion that are communicated to one or moreother vehicle subsystems. The motion planning intelligence 110 can alsoissue tasking commands 122 to the sensors 106 to exercise control oversensor data acquisition.

The system 100 of FIG. 1 effectively organizes the motion planningsystem 102 and the sensors suite 104 in a master-slave hierarchicalrelationship, which places large burdens on the motion planning system102. These processing burdens result in motion decision-making delaysarising from the amount of time it takes for the motion planning system102 to ingest, store, retrieve, and analyze the sensor data.

As a technical improvement in the art, the inventors disclose a morecollaborative model of decision-making as between the one or more of thesensors 106 and the motion planning system 102, whereby some of theintelligence regarding object and anomaly detection are moved into oneor more of the sensors 106. In the event that the intelligent sensordetects an object of concern from the sensor data, the intelligentsensor can notify the motion planning system 102 via priority messagingor some other “fast path” notification. This priority messaging canserve as a vector interrupt that interrupts the motion planning system102 to allow for the motion planning system 102 to quickly focus on thenewly detected threat found by the intelligent sensor. Thus, unlike themaster-slave relationship shown by FIG. 1, an example embodiment of anew faster approach to sensor-based motion planning can employ more of apeer-to-peer model for anomaly detection coupled with a capability forone or more intelligent sensors to issue priority messages/interrupts tothe motion planning system. With this model, threats detected by theintelligent sensor can be pushed to the top of the data stack underconsideration by the motion planning system 102.

The inventors also disclose a “fast path” for sensor tasking wherethreat detection by the intelligent sensor can trigger the intelligentsensor to insert new shot requests into a pipeline of sensor shotsrequested by the motion planning system. This allows the intelligentsensor to quickly obtain additional data about the newly detected threatwithout having to wait for the slower decision-making that would beproduced by the motion planning system.

Furthermore, in an example embodiment, the inventors disclose that theintelligent sensor can be a ladar system that employs compressivesensing to reduce the number of ladar shots required to capture a frameof sensor data. When such a ladar system is combined with thecollaborative/shared model for threat detection, where the ladar systemcan issue “fast path” priority messages to the motion planning systemregarding possible threats, latency is further reduced. As used herein,the term “ladar” refers to and encompasses any of laser radar, laserdetection and ranging, and light detection and ranging (“lidar”).

Further still, the inventors disclose example embodiments where a camerais co-bore sited with a ladar receiver to provide low latency detectionof objects in a field of view for a ladar system. A frequency-based beamsplitter can be positioned to facilitate sharing of the same field ofview by the ladar receiver and the camera.

Furthermore, the inventors also disclose example embodiments where tightclusters of overlapping ladar pulse shots are employed to facilitate thecomputation of motion data of objects on an intraframe basis. Thisallows the development of robust kinematic models of objects in a fieldof view on a low latency basis.

Moreover, the inventors also disclose techniques for selecting a definedshot list frame from among a plurality of defined shot list frames foruse by a ladar transmitter to identify where ladar pulses will betargeted with respect to a given frame. These selections can be madebased on processed data that represents one or more characteristics of afield of view for the ladar system, and the selections of shot listframes can vary from frame-to-frame.

These and other features and advantages of the present invention will bedescribed hereinafter to those having ordinary skill in the art.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 discloses a conventional motion planning system for vehicleautonomy.

FIG. 2 discloses a motion planning system for vehicle autonomy inaccordance with an example embodiment that includes a fast pathnotifications regarding threat detections from an intelligent ladarsystem.

FIG. 3A discloses an example embodiment of an intelligent ladar systemthat can provide fast path notifications regarding threat detections.

FIG. 3B discloses another example embodiment of an intelligent ladarsystem that can provide fast path notifications regarding threatdetections.

FIG. 4 discloses an example embodiment of a ladar transmitter subsystemfor use in an intelligent ladar system such as that shown by FIG. 3A or3B.

FIG. 5A discloses an example embodiment of a ladar receiver subsystemfor use in an intelligent ladar system such as that shown by FIG. 3A or3B.

FIG. 5B discloses another example embodiment of a ladar receiversubsystem for use in an intelligent ladar system such as that shown byFIG. 3A or 3B.

FIG. 6A-6C show examples of “fast path” ladar tasking.

FIG. 7 shows an example sequence of motion planning operations for anexample embodiment together with comparative timing examples relative toa conventional system.

FIG. 8 discloses example process flows for collaborative detection ofvarious kinds of threats.

FIG. 9 discloses an example protection circuit to protect against highenergy interferers.

FIGS. 10A-10D show example embodiments where a co-bore sited a cameraaids the ladar receiver to improve the latency by which ladar data isprocessed.

FIGS. 11A and 11B show example process flows where tight clusters ofladar shots are used to facilitate computations of intraframe motiondata for a target.

FIG. 12A shows an example cluster pattern for ladar shots to facilitateintraframe motion computations.

FIG. 12B shows an example data table for beam clusters and velocityestimations.

FIG. 13A shows an example process flow for frame-by-frame selection ofshot list frames for a ladar system.

FIGS. 13B-13I show examples of different types of shot list frames thatcan be supported by the process flow of FIG. 13A.

FIG. 14 shows an example scenario where low latency threat detection canbe advantageous.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

FIG. 2 discloses an example system 200 for vehicle autonomy with respectto motion planning. In this example, the motion planning system 202interacts with a sensor such as an intelligent ladar system 206 in amanner where the intelligent ladar system 206 is able to provide fastpath notifications regarding detected threats. Unlike the conventionalmaster-slave hierarchical relationship between a motion planning systemand sensor, the example embodiment of FIG. 2 employs a collaborativemodel of decision-making as between an intelligent ladar system 206 andthe motion planning system 202, whereby some of the intelligenceregarding object and anomaly detection is positioned in the intelligentladar system 206. Also, it should be understood that the system 200 mayinclude sensors other than intelligent ladar system 206 that provideinformation to the motion planning system 202 (e.g., one or morecameras, one or more radars, one or more acoustic sensors, one or morevehicle telematics sensors (e.g., a brake sensor that can detect lockedbrakes; a tire sensor that can detect a flat tire), etc.), although forease of illustration such other sensors are omitted from FIG. 2. Itshould be understood that one or more of such other sensors may alsooptionally employ the collaborative decision-making techniques disclosedherein if desired by a practitioner.

The intelligent ladar system 206 provides the motion planning system 202with ladar frames 220 for use in the obstacle detection and motionplanning process. These ladar frames 220 are generated in response tothe ladar system firing ladar pulses 260 at targeted range points andthen receiving and processing reflected ladar pulses 262. Exampleembodiments for a ladar system that can be used to support the ladartransmit and receive functions of the intelligent ladar system 206 aredescribed in U.S. patent application Ser. No. 62/038,065 (filed Aug. 15,2014) and U.S. Pat. App. Pubs. 2016/0047895, 2016/0047896, 2016/0047897,2016/0047898, 2016/0047899, 2016/0047903, 2016/0047900, 2017/0242108,2017/0242105, 2017/0242106, 2017/0242103, 2017/0242104, and2017/0307876, the entire disclosures of each of which are incorporatedherein by reference.

Sensor data ingest interface 208 within the motion planning system 202receives the ladar frames data 220 from the intelligent ladar system 206and stores the ladar frames data 220 in a sensor data repository 230where it will await processing. Motion planning intelligence 210 withinthe motion planning system 202 issues read or query commands 224 to thesensor data repository 230 and receives the requested sensor data asresponses 226 to the queries 224. The intelligence 210 then analyzesthis retrieved sensor data to make decisions 228 about vehicle motionthat are communicated to one or more other vehicle subsystems 232. Themotion planning intelligence 210 can also issue shot list taskingcommands 222 to the intelligent ladar system 206 to exercise controlover where and when the ladar pulses 260 are targeted.

As an improvement over conventional motion planning systems, theintelligent ladar system 206 also provides a notification to the sensordata ingest interface 208 that notifies the motion planning system 202about a detected threat or other anomaly. This notification can take theform of a priority flag 250 that accompanies the ladar frames data 220.Together, the priority flag 250 and ladar frames data 220 can serve as a“fast” path notification 252 for the motion planning intelligence 210.This is in contrast to the “slow” path 254 whereby the motion planningintelligence makes decisions 228 only after new ladar frames data 220have been ingested and stored in the sensor data repository 230 andretrieved/processed by the motion planning intelligence 210. Ifintelligence within the intelligent ladar system 206 determines that athreat might be present within the ladar frames data 220, theintelligent ladar system 206 can set the priority flag 250 to “high” orthe like, whereupon the motion planning system is able to quicklydetermine that the ladar frames data 220 accompanying that priority flag250 is to be evaluated on an expedited basis. Thus, the priority flag250 can serve as a vector interrupt that interrupts the normalprocessing queue of the motion planning intelligence 210.

The priority flag 250 can take any of a number of forms. For example,the priority flag can be a simple bit value that is asserted “high” whena threat is detected by the intelligent ladar system 206 and asserted“low” when no threat is detected. A “high” priority flag 250 wouldinform the sensor data ingest interface 208 and motion planningintelligence 210 that the ladar frames data 220 which accompanies the“high” priority flag 250 is to be considered on a priority basis (e.g.,immediately, as the next frame(s) to be considered, and the like). Thepriority flag 250 can be provided to the motion planning system 202 as aseparate signal that is timed commensurately with the ladar frames data220, or it can be embedded within the ladar frames data 220 itself. Forexample, the intelligent ladar system 206 can include a header orwrapper with the frames of ladar data when it communicates the ladarframes data 220 to the motion planning system 202. This header/wrapperdata can include priority flag 250. The header/wrapper can be structuredin accordance with a communication protocol shared between theintelligent ladar system 206 and the motion planning system 202 topermit effective data communication between the two.

Further still, a practitioner may choose to implement a priority flag250 that communicates more than just the existence of a priority event.The priority flag 250 may also be configured to encode a type ofpriority event. For example, if the intelligent ladar system 206 is ableto detect and distinguish between different types of threats/anomalies,the intelligent ladar system 206 can encode the detected threat/anomalytype in a multi-bit priority flag 250. For example, if the intelligentladar system 206 is able to identify 4 different types ofthreats/anomalies, the priority flag 250 can be represented by 2 bits.This information about the type of threat/anomaly could then be used bythe motion planning intelligence 210 to further enhance and/oraccelerate its decision-making.

The sensor data ingest interface 208 can thus be configured to (1) storeladar frames 220 in sensor data repository 230 via the “slow” path 254(to keep the repository 230 current), and (2) pass ladar frames 220directly to the motion planning intelligence 210 via the “fast” path 252if so indicated by the priority flag 250. To accomplish this, theinterface 208 can include logic that reads the incoming priority flag250 from the intelligent ladar system 206. If the priority flag has theappropriate bit (or bits) set, then the sensor data ingest interface 208passes the accompanying ladar frames 220 to the motion planningintelligence 210. The priority flag 250 (or a signal derived from thepriority flag 250) can also be passed to the motion planningintelligence 210 by the sensor data ingest interface 208 when thepriority flag 250 is high.

The motion planning intelligence 210 can include logic for adjusting itsprocessing when the priority flag 250 is asserted. For example, themotion planning intelligence 210 can include buffers for holdingprocessing states and allowing context switching in response to vectorinterrupts as a result of the priority flag 250. To facilitate suchprocessing, the motion planning intelligence 210 can include a threadedstack manager that allows for switching between different threads ofprocessing (or simultaneous thread processing) to permit the motionplanning intelligence 210 to quickly focus on newly detected threats oranomalies.

FIG. 3A depicts an example embodiment for the intelligent ladar system206. The intelligent ladar system 206 can include a ladar transmitter302, ladar receiver 304, and a ladar system interface and control 306.The ladar system 206 may also include an environmental sensing system320 such as a camera. An example of a suitable ladar system with thisarchitecture is disclosed in the above-referenced and incorporatedpatent applications.

The ladar transmitter 304 can be configured to transmit a plurality ofladar pulses 260 toward a plurality of range points 310 (for ease ofillustration, a single such range point 310 is shown in FIG. 3A).

In example embodiments, the ladar transmitter 302 can take the form of aladar transmitter that includes scanning mirrors. Furthermore, in anexample embodiment, the ladar transmitter 302 uses a range point downselection algorithm to support pre-scan compression (which can bereferred herein to as “compressive sensing”). Such an embodiment mayalso include the environmental sensing system 320 that providesenvironmental scene data to the ladar transmitter 302 to support therange point down selection (see the dashed lines coming from the outputof the environmental sensing system 320 shown in FIG. 3A). Controlinstructions will instruct a laser source within the ladar transmitter302 when to fire, and will instruct the transmitter mirrors where topoint. Example embodiments of such ladar transmitter designs can befound in the above-referenced and incorporated patent applications.Through the use of pre-scan compression, such a ladar transmitter 302can better manage bandwidth through intelligent range point targetselection. Moreover, this pre-scan compression also contributes toreduced latency with respect to threat detection relative toconventional ladar systems because fewer range points need to betargeted and shot in order to develop a “picture” of the scene, whichtranslates to a reduced amount of time needed to develop that “picture”and act accordingly.

A ladar tasking interface 354 within the system interface and control306 can receive shot list tasking 222 from the motion planning system202. This shot list tasking 222 can define a shot list for use by theladar transmitter 302 to target ladar pulses 260 toward a plurality ofrange points 310 within a scan area. Also, the motion planningintelligence 210 (see FIG. 2) can receive feedback 234 from one or morevehicle subsystems 232 for use in the obstacle detection and motionplanning process. Intelligence 210 can use this feedback 234 to helpguide the formulation of queries 224 into the sensor data repository 230and/or shot list tasking 222 for the intelligent ladar system 206.Furthermore, the vehicle subsystem(s) 232 can provide a failsafe shotlist 238 to the motion planning intelligence 210 for passing on to theintelligent ladar system 206. Together, the shot list tasking 222 andfailsafe shot list 238 can serve as an “emergency” notification path 236for the intelligent ladar system 206. This is in contrast to the queries224 whereby the motion planning intelligence 210 sends and storesvehicle subsystems 232 data in the sensor data repository 230. As anexample, failsafe shots might arise from vehicle subsystem selfdiagnostic failures. For example if the GPS readings for the vehicle areerrant, or the odometer is malfunctioning, the ladar system 206 can beused to recalibrate and/or assume speed and location provisioning forthe vehicle until it can safely extract itself from traffic. Anotherexample of failsafe shots might be from the shock absorbers experiencingheavy torque. A shot list can provide independent assessment of pitchyaw and roll experienced from a transient road depression.

Ladar receiver 304 receives a reflection 262 of this ladar pulse fromthe range point 310. Ladar receiver 304 can be configured to receive andprocess the reflected ladar pulse 262 to support a determination ofrange point distance [depth] and intensity information. In addition, thereceiver 304 can determine spatial position information [in horizontaland vertical orientation relative to the transmission plane] by anycombination of (i) prior knowledge of transmit pulse timing, and (ii)multiple detectors to determine arrival angles. An example embodiment ofladar receiver 304 can be found in the above-referenced and incorporatedpatent applications.

The range point data generated by the ladar receiver 304 can becommunicated to frame processing logic 350. This frame processing logic350 can be configured to build ladar frames 220 from the range pointdata, such as from a set of range point returns in a sampled region of afield of view. Techniques such as frame differencing, from historicalpoint cloud information, can be used. The frame(s) generated along thispath can be very sparse, because its purpose is to detect threats. Forexample if the task at hand is ensuring that no one is violating a redlight at an intersection (e.g., moving across the intersection in frontof a ladar-equipped car), a frame can simply be a tripwire of rangepoints set to sense motion across the road leading up to theintersection.

As an example, FIG. 3A shows frame processing logic 350 as being presentwithin the system interface and control 306. However, it should beunderstood that this frame processing logic 350 could be deployedelsewhere, such as within the ladar receiver 304 itself.

The frame processing logic 350 may also include threat detection logicin order to provide the ladar system 206 with sufficient intelligencefor collaborating with the motion planning system 202 regardingpotential threats/anomalies. As part of this threat detection, the frameprocessing logic 350 can build a point cloud 352 from the range pointdata received from the ladar receiver 304. The point cloud 352 can be anaggregation of points in space, denoted as a function of angles, range,and intensity, which are time-stamped within a framed field of regard,stored historically, and tracked. Accordingly, the point cloud 352 caninclude historical data such as geometric position, intensity, rangeextent, width, and velocity for prior range point returns and sensordata (and object data derived therefrom). An example of using a pointcloud 352 to perceive threats would be to look at the time history ofpoint cloud objects. A vehicle that is erratically swerving, forexample, is a threat best revealed by looking at the point cloud“wiggle” around the object representing said vehicle. The point cloud352 could be queried for as long back in time as the vehicle's ladarfield of view intersects past collected data. Thus, the point cloud 352can serve as a local repository for sensor data that can be leveraged bythe ladar system 206 to assess potential threats/anomalies. Furthermore,the point cloud 352 can also store information obtained from sensorsother than ladar (e.g., a camera).

The threat detection intelligence can be configured to exploit the pointcloud 352 and any newly incoming range point data (and/or other sensordata) to determine whether the field of view as detected by the ladarsystem 206 (and/or other sensor(s)) includes any threats or anomalies.To perform this processing, the threat detection intelligence can employstate machines that track various objects in a scene over time to assesshow the locations and appearance (e.g., shape, color, etc.) change overtime. Based on such tracking, the threat detection intelligence can makea decision regarding whether the priority flag 250 should be set “high”or “low”. Examples of various types of such threat detection aredescribed in connection with FIG. 8 below.

FIG. 4 depicts an example embodiment for the ladar transmitter 302. Theladar transmitter 302 can include a laser source 402 in opticalalignment with laser optics 404, a beam scanner 406, and transmissionoptics 408. These components can be housed in a packaging that providesa suitable shape footprint for use in a desired application. Forexample, for embodiments where the laser source 402 is a fiber laser orfiber-coupled laser, the laser optics 404, the beam scanner 406, and anyreceiver components can be housed together in a first packaging thatdoes not include the laser source 402. The laser source 402 can behoused in a second packaging, and a fiber can be used to connect thefirst packaging with the second packaging. Such an arrangement permitsthe first packaging to be smaller and more compact due to the absence ofthe laser source 402. Moreover, because the laser source 402 can bepositioned remotely from the first packaging via the fiber connection,such an arrangement provides a practitioner with greater flexibilityregarding the footprint of the system.

Based on control instructions, such as a shot list 400 received fromsystem control 306, a beam scanner controller 410 can be configured tocontrol the nature of scanning performed by the beam scanner 406 as wellas control the firing of the laser source 402. A closed loop feedbacksystem 412 can be employed with respect to the beam scanner 406 and thebeam scanner controller 410 so that the scan position of the beamscanner 406 can be finely controlled, as explained in theabove-referenced and incorporated patent applications.

The laser source 402 can be any of a number of laser types suitable forladar pulse transmissions as described herein.

For example, the laser source 402 can be a pulsed fiber laser. Thepulsed fiber laser can employ pulse durations of around 1-4 ns, andenergy content of around 0.1-100 μJ/pulse. The repetition rate for thepulsed laser fiber can be in the kHz range (e.g., around 1-500 kHz).Furthermore, the pulsed fiber laser can employ single pulse schemesand/or multi-pulse schemes as described in the above-referenced andincorporated patent applications. However, it should be understood thatother values for these laser characteristics could be used. For example,lower or higher energy pulses might be employed. As another example, therepetition rate could be higher, such as in the 10's of MHz range(although it is expected that such a high repetition rate would requirethe use of a relatively expensive laser source under current marketpricing).

As another example, the laser source 402 can be a pulsed IR diode laser(with or without fiber coupling). The pulsed IR diode laser can employpulse durations of around 1-4 ns, and energy content of around 0.01-10μJ/pulse. The repetition rate for the pulsed IR diode fiber can be inthe kHz or MHz range (e.g., around 1 kHz-5 MHz). Furthermore, the pulsedIR diode laser can employ single pulse schemes and/or multi-pulseschemes as described in the above-referenced and incorporated patentapplications.

The laser optics 404 can include a telescope that functions to collimatethe laser beam produced by the laser source 402. Laser optics can beconfigured to provide a desired beam divergence and beam quality. Asexample, diode to mirror coupling optics, diode to fiber couplingoptics, and fiber to mirror coupling optics can be employed dependingupon the desires of a practitioner.

The beam scanner 406 is the component that provides the ladartransmitter 302 with scanning capabilities such that desired rangepoints can be targeted with ladar pulses 260. The beam scanner 406receives an incoming ladar pulse from the laser source 402 (by way oflaser optics 404) and directs this ladar pulse to a desired downrangelocation (such as a range point on the shot list) via reflections frommovable mirrors. Mirror movement can be controlled by one or moredriving voltage waveforms 416 received from the beam scanner controller410. Any of a number of configurations can be employed by the beamscanner 406. For example, the beam scanner can include dualmicroelectromechanical systems (MEMS) mirrors, a MEMS mirror incombination with a spinning polygon mirror, or other arrangements. Anexample of suitable MEMS mirrors is a single surface tip/tilt/pistonMEMS mirrors. By way of further example, in an example dual MEMS mirrorembodiment, a single surface tip MEMS mirror and a single surface tiltMEMS mirror can be used. However, it should be understood that arrays ofthese MEMS mirrors could also be employed. Also, the dual MEMS mirrorscan be operated at any of a number of frequencies, examples of which aredescribed in the above-referenced and incorporated patent applications,with additional examples being discussed below. As another example ofother arrangements, a miniature galvanometer mirror can be used as afast-axis scanning mirror. As another example, an acousto-opticdeflector mirror can be used as a slow-axis scanning mirror.Furthermore, for an example embodiment that employs a spiral dynamicscan pattern, the mirrors can be resonating galvanometer mirrors. Suchalternative mirrors can be obtained from any of a number of sources suchas Electro-Optical Products Corporation of New York. As another example,a photonic beam steering device such as one available from VescentPhotonics of Colorado can be used as a slow-axis scanning mirror. Asstill another example, a phased array device such as the one beingdeveloped by the DARPA SWEEPER program could be used in place of thefast axis and/or slow axis mirrors. More recently, liquid crystalspatial light modulators (SLMs), such as those offered by BoulderNonlinear Systems, Meadowlark, and Beamco, can be considered for use.Furthermore, quantum dot SLMs have been recently proposed (see TechnicalUniversity of Dresden, 2011 IEEE Conference on Lasers andElectro-Optics), which hold promise of faster switching times when usedin example embodiments.

Also, in an example embodiment where the beam scanner 406 includes dualmirrors, the beam scanner 406 may include relay imaging optics betweenthe first and second mirrors, which would permit that two small fastaxis mirrors be used (e.g., two small fast mirrors as opposed to onesmall fast mirror and one long slower mirror).

The transmission optics 408 are configured to transmit the ladar pulseas targeted by the beam scanner 406 to a desired location through anaperture. The transmission optics 408 can have any of a number ofconfigurations depending upon the desires of a practitioner. Forexample, the environmental sensing system 320 and the transmitter 302can be combined optically into one path using a dichroic beam splitteras part of the transmission optics 408. As another example, thetransmission optics can include magnification optics as described in theabove-referenced and incorporated patent applications or descoping[e.g., wide angle] optics. Further still, an alignment pickoff beamsplitter can be included as part of the transmission optics 408.

FIG. 5A depicts an example embodiment for the ladar receiver 304.Readout circuitry within the ladar receiver 304 can employs amultiplexer 504 for selecting which sensors 502 within a detector array500 are passed to a signal processing circuit 506. In an exampleembodiment, the sensors 502 may comprise a photodetector coupled to apre-amplifier. In an example embodiment, the photodetector could be aPIN photodiode and the associated pre-amplifier could be atransimpedance amplifier (TIA). In the example embodiment depicted byFIG. 5A, a detector array 500 comprising a plurality ofindividually-addressable light sensors 502 is used to sense ladar pulsereflections 262. Each light sensor 502 can be characterized as a pixelof the array 500, and each light sensor 502 will generate its own sensorsignal 510 in response to incident light. Thus, the array 500 cancomprise a photodetector with a detection region that comprises aplurality of photodetector pixels. The embodiment of FIG. 5A employs amultiplexer 504 that isolates the incoming sensor signals 510 that arepassed to the signal processing circuit 506 at a given time. In doingso, the embodiment of FIG. 5A provides better received SNR, especiallyagainst ambient passive light, relative to ladar receiver designs suchas those disclosed by U.S. Pat. No. 8,081,301 where no capability isdisclosed for selectively isolating sensor readout. Thus, the signalprocessing circuit 506 can operate on a single incoming sensor signal510 (or some subset of incoming sensor signals 510) at a time.

The multiplexer 504 can be any multiplexer chip or circuit that providesa switching rate sufficiently high to meet the needs of detecting thereflected ladar pulses. In an example embodiment, the multiplexer 504multiplexes photocurrent signals generated by the sensors 502 of thedetector array 500. However, it should be understood that otherembodiments may be employed where the multiplexer 504 multiplexes aresultant voltage signal generated by the sensors 502 of the detectorarray 500. Moreover, in example embodiments where the ladar receiver 304of FIG. 5A is paired with a scanning ladar transmitter 302 that employspre-scan compressive sensing (such as the example embodiments employingrange point down selection that are described above and in theabove-referenced and incorporated patent applications), the selectivetargeting of range points provided by the ladar transmitter 302 pairswell with the selective readout provided by the multiplexer 504 so thatthe receiver 304 can isolate detector readout to pixels of interest inan effort to improve SNR.

A control circuit 508 can be configured to generate a control signal 512that governs which of the incoming sensor signals 510 are passed tosignal processing circuit 506. In an example embodiment where the ladarreceiver 304 is paired with a scanning ladar transmitter 302 thatemploys pre-scan compressive sensing according to a scan pattern, thecontrol signal 512 can cause the multiplexer 504 to selectively connectto individual light sensors 502 in a pattern that follows thetransmitter's shot list (examples of the shot list that may be employedby such a transmitter 302 are described in the above-referenced andincorporated patent applications). The control signal 512 can selectsensors 502 within array 500 in a pattern that follows the targeting ofrange points via the shot list. Thus, if the transmitter 302 istargeting pixel x,y in the scan area with a ladar pulse 260, themultiplexer 504 can generate a control signal 512 that causes a readoutof pixel x,y from the detector array 500.

It should be understood that the control signal 512 can be effective toselect a single sensor 502 at a time or it can be effective to selectmultiple sensors 502 at a time in which case the multiplexer 504 wouldselect a subset of the incoming sensor signals 510 for furtherprocessing by the signal processing circuit 506. Such multiple sensorscan be referred to as composite pixels (or superpixels). For example,the array 500 may be divided into a J×K grid of composite pixels, whereeach composite pixel is comprised of X individual sensors 502. Summercircuits can be positioned between the detector array 500 and themultiplexer 504, where each summer circuit corresponds to a singlecomposite pixel and is configured to sum the readouts (sensor signals510) from the pixels that make up that corresponding composite pixel.

It should also be understood that a practitioner may choose to includesome pre-amplification circuitry between the detector array 500 and themultiplexer 504 if desired.

If desired by a practitioner, the threat detection intelligence andpoint cloud 352 discussed above can be included as part of the signalprocessing circuit 506. In such a case, the signal processing circuit506 can generate the frame data 220 and corresponding priority flag 250.

In the example of FIG. 5B, the signal processing circuit 506 comprisesan amplifier 550 that amplifies the selected sensor signal(s), ananalog-to-digital converter (ADC) 552 that converts the amplified signalinto a plurality of digital samples, and a field programmable gate array(FPGA) 554 that is configured to perform a number of processingoperations on the digital samples to generate the processed signal data.It should be understood that the signal processing circuit 506 need notnecessarily include an FPGA 554; the processing capabilities of thesignal processing circuit 506 can be deployed in any processor suitablefor performing the operations described herein, such as a centralprocessing unit (CPU), micro-controller unit (MCU), graphics processingunit (GPU), digital signal processor (DSP), and/or application-specificintegrated circuit (ASIC) or the like. However, the inventors note thatan FPGA 554 is expected to provide suitably high performance and lowprocessing latency that will beneficially contribute to low latencythreat detection.

The amplifier 550 can take the form of a low noise amplifier such as alow noise RF amplifier or a low noise operational amplifier. The ADC 552can take the form of an N-channel ADC.

The FPGA 554 includes hardware logic that is configured to process thedigital samples and ultimately return information about range and/orintensity with respect to the range points based on the reflected ladarpulses. In an example embodiment, the FPGA 554 can be configured toperform peak detection on the digital samples produced by the ADC 552.In an example embodiment, such peak detection can be effective tocompute range information within +/−10 cm. The FPGA 554 can also beconfigured to perform interpolation on the digital samples where thesamples are curve fit onto a polynomial to support an interpolation thatmore precisely identifies where the detected peaks fit on the curve. Inan example embodiment, such interpolation can be effective to computerange information within +/−5 mm.

Moreover, the FPGA 554 can also implement the threat detectionintelligence discussed above so that the signal processing circuit 506can provide frame data 220 and priority flag 250 to the motion planningsystem 202.

When a receiver 304 which employs a signal processing circuit 506 suchas that shown by FIG. 5B is paired with a ladar transmitter 302 thatemploys compressive sensing as described above and in theabove-referenced and incorporated patent applications, the receiver 304will have more time to perform signal processing on detected pulsesbecause the ladar transmitter would put fewer ladar pulses in the airper frame than would conventional transmitters, which reduces theprocessing burden placed on the signal processing circuit 506. Moreover,to further improve processing performance, the FPGA 554 can be designedto leverage the parallel hardware logic resources of the FPGA such thatdifferent parts of the detected signal are processed by differenthardware logic resources of the FPGA at the same time, thereby furtherreducing the time needed to compute accurate range and/or intensityinformation for each range point.

Furthermore, the signal processing circuit of FIG. 5B is capable ofworking with incoming signals that exhibit a low SNR due to the signalprocessing that the FPGA 554 can bring to bear on the signal data inorder to maximize detection. The SNR can be further enhanced by varyingthe pulse duration on transmit. For example, if the signal processingcircuit reveals higher than usual clutter (or the presence of otherlaser interferers) at a range point, this information can be fed back tothe transmitter for the next time that the transmitter inspects thatrange point. A pulse with constant peak power but extended by a multipleof G will have G times more energy. Simultaneously, it will possess Gtimes less bandwidth. Hence, if we low pass filter digitally, the SNR isexpected to increase by G^(1/2), and the detection range for fixedreflectivity is expected to increase by G^(1/4). This improvement isexpected to hold true for all target-external noise sources: thermalcurrent noise (also called Johnson noise), dark current, and background,since they all vary as √{square root over (G)}. The above discussionentails a broadened transmission pulse. Pulses can at times be stretcheddue to environmental effects. For example, a target that has a projectedrange extent within the beam diffraction limit will stretch the returnpulse. Digital low pass filtering is expected to improve the SNR here by√{square root over (G)} without modifying the transmit pulse. Thetransmit pulse duration can also be shortened, in order to reduce pulsestretching from the environment. Pulse shortening, with fixed pulseenergy, also increases SNR, provided the peak power increase isachievable. The above analysis assumes white noise, but the practitionerwill recognize that extensions to other noise spectrum arestraightforward.

While examples of suitable designs for ladar transmitter 302 and ladarreceiver 304 are disclosed in the above-referenced and incorporatedpatent applications, the inventors further note that practitioners maychoose alternate designs for a ladar transmitter and ladar receiver foruse with intelligent ladar system 206 if desired.

FIG. 3B discloses another example embodiment of an intelligent ladarsystem 206. In the example of FIG. 3B, the ladar system 206 alsoincludes a “fast” path 360 for shot list tasking. As indicated above,the threat detection intelligence 350 can be configured to detectregions within a field of view that correspond to a potential threat oranomaly. In order to obtain more information from this region ofconcern, it is desirable to target the ladar transmitter 302 onto thatregion and fire additional ladar pulses 260 toward this region. However,if the motion planning system 202 is the entity that makes decisionsabout where to target the ladar transmitter 302, the inventors note thata fair amount of latency will be introduced into the targeting of theladar transmitter 302 because the ladar transmitter will need to waitfor the information to be communicated to, ingested by, and consideredby the motion planning system 202 before the motion planning system 202can make a decision about which region(s) should be targeted by theladar transmitter 302. Further still, latency would be added while theladar transmitter awaits the transmission of these targetinginstructions from the motion planning system 202. The fast path 360,shown by FIG. 3B, bypasses this longer decision-making path.

With FIG. 3B, when the threat detection intelligence within the frameprocessing logic 350 detects an area of concern with the scan area/fieldof view, the threat detection intelligence can re-task the ladartransmitter 302 by identifying the area of concern to the ladar taskinginterface 354 via fast path 360. This direct feed into the ladar taskinginterface 354 allows the ladar tasking interface 354 to quickly insertnew shots into the pipelined shot list 400 that is used to control thetargeting of the ladar transmitter 302. An example of how the new rangeshots might be obtained can be as follows: suppose that motion is sensedeither from a video camera or from the ladar point cloud in a regionclose enough to the vehicle's planned path to be a threat. Then, the newshots can be identified as the set of voxels that are both (i) near thesensed motion's geometric location (ii) along the planned trajectory ofthe vehicle, and (iii) likely to resolve the nature of the sensedmotion. This last item (iii) is best considered in the context ofdetecting an animal crossing the road—is the motion from leaves or ananimal in transit? A motion model for both the animal and diffusevegetation motion can be used to assess the best shot position toseparate these hypotheses.

Two examples of how new shots can be allocated by the system caninclude: (1) threat detection within 350 tells a tasking system totarget a general area of concern, and probe shots are defined by thetasking system to build out the scene (for example, an ambiguous blobdetection from radar could trigger a list of probe shots to build outthe threat), and (2) threat intelligence receives a specific datasetfrom a source that gives more clarity to the threat in order to decideon specific set of range points (for example, a camera provides contrastinformation or detects edges, where the high contrast and/or edge pixelswould correspond to specific range points for new shots).

FIGS. 6A-6C depict examples of how new shots can be inserted into a shotlist 400 via the fast path 360. FIG. 6A shows how a shot list 400 isused by a scheduler 600 to control the targeting of range points (seethe star in FIG. 6A which represents a targeted range point) viascanning mirrors 602 and 604. The shot list 400 comprises a sequence ofshots to be fired by the ladar transmitter 302. Each shot on shot listcan be identified by coordinates within the scan area for the targetedrange points or other suitable mechanisms for informing the ladartransmitter as to which range points are to be targeted (e.g., edgesdetect in an image). For example, one might have a standard scan patternto maintain synoptic knowledge of the environment in perceivednon-threatening conditions, and shot list 400 could represent theseshots. For example, raster scan or foveated patterns could be used toprobe a scene in order to detect hidden threats. To target Shot 1 fromthe shot list 400, laser source 402 is fired when the scanning mirrors602 and 604 are positioned such that the ladar pulse will be projectedtoward the targeted range point for Shot 1. The ladar pulse will thenstrike the range point and reflect back upon the detector array 500 (viaa receiver lens 610 or the like). One or more sensors 502 of the array500 will then produce a signal 510 (e.g. a readout current) that can beprocessed to learn information about the targeted range point.

FIG. 6B shows how this signal 510 can be leveraged to control re-taskingof the ladar transmitter 302 via the fast path 360. With FIG. 6B, Shot 2from the shot list 400 is used to control the targeting and firing ofthe ladar transmitter 302. Meanwhile, if the threat detectionintelligence determines that new ladar shots are needed to gain moreinformation about a potential threat/anomaly, the ladar taskinginterface 354 can generate one or more ladar shot insertions 650. Theseshot list insertions 650 can then be inserted into shot list 400 as thenext sequence of shots to be taken by the ladar transmitter 302 (seeFIG. 6C). Accordingly, the ladar transmitter 302 can be quicklyre-tasked to target regions of interest that are found by the ladarsystem 206's threat detection intelligence. It is also possible for themotion planner itself to query the ladar system, which a practitionermay choose to define as over-riding the interrupt that wasself-generated by the ladar system. For example, vehicle pitch or yawchanges could cue a foveated scan corresponding to the determineddirection of motion.

FIG. 7 shows an example sequence of motion planning operations for anexample embodiment together with comparative timing examples relative toa conventional system.

Current conventional ladar systems employ raster scans, which generatepoint clouds in batch mode and suffer from high latency (as do videocameras in isolation). FIG. 7 shows a scenario where a conventionalmotion planner derived from a raster scan ladar system will have scenedata interpretation delayed by 60+ feet of closure with respect to thevehicle moving at 100 kilometers per hour (kph) using U.S. Department ofTransportation data on braking response times, even with generousassumptions, whereas an example embodiment of the intelligent ladar andmotion planning system disclosed herein is expected to have less thanone foot of position latency. In other words, a motion planner that usesthe inventive techniques described herein is expected to obtainbehavioral information about objects in the scene of potential threatwhen the objects have moved only one foot closer to the vehicle, versus60+ feet as would be the case with a conventional motion planningsystem. FIG. 7 discloses various steps in a motion planning operation,and the lower portion of FIG. 7 discloses estimates regarding latencyand distance required for each step of the sequence (where the numberspresented are in terms of cumulative time and distance) for theconventional raster scan ladar approach (labeled as “OLD”) and theinventive techniques described herein (labeled as “NEW”). Accordingly,FIG. 7 discloses how example embodiments of the invention are expectedto provide microsecond probing and exploitation corresponding tomillisecond response time updating of motion planning, an importantcapability for autonomous vehicles to be able to counter rare, butpotentially fatal, emerging obstacles, such as deer crossing,red-stop-sign moving violators at intersections, and motorcycle vehiclepassing. All three of these may or will require motion planning updatesat millisecond time scales if accidents are to be reliably anddemonstrably avoided.

Recent advances by Luminar have shown that ladar can achieve detectionranges, even against weak 10% reflectivity targets, at 200 m+ ranges,when properly designed. This is helpful for providing response times andsaving lives. For example, consider a motorcycle, detected at 200 m.This range for detection is useful, but in even modest traffic themotorcycle will likely be occluded at some time before it nears orpasses the vehicle. For example, suppose the motorcycle is seen at 200m, and then is blocked by cars between it and a ladar-equipped vehicle.Next suppose the motorcycle reappears just as the motorcycle is passinganother vehicle, unaware it is on a collision course with theladar-equipped vehicle, at 100 m range. If both it and theladar-equipped vehicle are moving at 60 mph (about 100 kph), the closingspeed is around 50 m/s. Having detected the motorcycle at 200 m willhelp the point cloud exploiter to reconfirm its presence when itreappears, but what about latency? If 2 seconds collision warning arerequired in order to update motion planning and save lives there is notime to spare, every millisecond counts.

A ladar system that requires two or more scans to confirm a detection,and which updates at 100 millisecond rates will require ⅕^(th) of asecond to collect data let alone sense, assess, and respond [motion planmodification]. This is where an example embodiment of the disclosedinvention can provide significant technical advances in the art. Throughan example embodiment, the inventors expect to reduce the “sense-to-planmodification” stage down to around 10 milliseconds (see FIG. 7). Thevalue of this improvement is that now the ladar-equipped vehicle canrespond to erratic behavior and unpredictable events, such asunobservant lane passers, at 30-50 m ranges, with one second or more toexecute an evasive maneuver. At ½ second response time, which is typicalfor conventional pipelined raster scanned systems, this distance extendsto 50 m-70 m, which is problematic because the further away the target,the more likely it will be blocked, or at low reflectivity, it will notbe detected at all.

Consider a second scenario, whereby a deer, a cyclist, or a vehicleblindsides the ladar-equipped vehicle by crossing the street in front ofthe ladar-equipped vehicle without warning. A system that scans, andconfirms, every 200 ms may well fail to detect such a blindside event,let alone detect it in time for motion updating for collision avoidance.Accordingly, the speed advantage provided by an example embodiment ofthe disclosed invention is far more than a linear gain, because thelikelihood of blindsiding collisions, whether from humans or animals,increases as less warning time is conferred.

Accordingly, the inventors believe there is a great need in the art fora system capable of motion planning updates within 200 ms or less(including processor latency), and the inventors for purposes ofdiscussion have chosen a nominal 10 millisecond delay from“sensor-to-motion planning update” as a benchmark.

The sequence of FIG. 7 begins with a scheduler 600 for an intelligentsensor detecting an obstacle at step 702 that may impact the motionpath. The intelligent sensor can be a wide field of view sensor such asthe intelligent ladar system 206 that provides a cue that there is apotential, but unvalidated, danger in the environment. If the sensor isanother heterogeneous sensor, not the ladar system itself, this process200 can take on two cases in an example embodiment.

The first case is what can be called “selective sensing”, whereby thescheduler 600 directs the ladar source 402, such as a direct probe casedescribed above. In this instance, the ladar system 206 is used toobtain more detailed information, such as range, velocity, or simplybetter illumination, when the sensor is something like a camera[infrared or visual] or the like. In the above, we assume that an objecthas been identified, and the ladar system is used to improve knowledgeabout said object. In other words, based on the cueing, sensing shotsare selected for the ladar system to return additional information aboutthe object. In “comparative” sensing, another scheduling embodiment, thesituation is more nuanced. With comparative sensing, the presence orabsence of an object is only inferred after the ladar data and the videoobject is obtained. This is because changes in a video may or may not beassociated with any one object. Shimmering of light might be due tovarious static objects at various distances (e.g., a non-threat), orfrom something such as reflections off a transiting animal's coat (e.g.,a threat). For example, a change in an image frame-to-frame will quicklyindicate motion, but the motion will blur the image and therefore makeit difficult to assess the form and shape of the moving object. Theladar system 206 can complement the passive image sensor. By selectingthe blur area for further sensing, the ladar system 206 can probe anddetermine a crisp 3D image. This is because the image will blur formotions around 10 Hz or so, while the ladar system 206 can sample theentire scene within 100 s of nanoseconds, hence no blurring. Comparisoncan be performed on post-ladar image formation, to assess whether theladar returns correspond to the blur region or static background.

As another example, the bidirectional feedback loop between two sensors(such as the ladar system and a cueing sensor) could provide requisiteinformation for motion planning based on the inherent nature ofcounterbalanced data review and verification.

Selective and comparative sensing can also result from sensorself-cueing. For a selective example consider the following. Objectmotion is detected with a ladar system 206, and then the ladar system(if it has intelligent range point capability via compressive sensing orthe like) can be tasked to surround the object/region of interest with asalvo of ladar shots to further characterize the object. For acomparative example, consider revisiting a section of road where anobject was detected on a prior frame. If the range or intensity returnchanges (which is by definition an act of comparison), this can becalled comparative sensing. Once an object has been detected, a higherperception layer, which can be referred to as “objective” sensing isneeded before a reliable interrupt is to be proffered to the automotivetelematics control subsystem. It desirable for the ladar system 206, tobe low latency, to be able to quickly slew its beam to the cued object.This requires rapid scan (an example of which shown is in FIGS. 6A-C asa gimbaled pair of azimuth and elevation scanning mirrors, 602 and 604.Two-dimensional optical phased array ladar could be used as well, andthe gimbals can be replaced by any manner of micromechanical scanmirrors. Examples of MEMS systems, being mechanical scanning, arespatial light modulators using liquid crystal such as offered by BoulderNonlinear Systems and Beamco.

Returning to FIG. 7, step 700 detects of a potential threat, withsubsequent sensor interpretation. The interpretation might be suddenmotion from the side of the road into the road (e.g. a tree branch,benign, or a deer leaping in front of the ladar-equipped car, a threat).A simple video exploitation that senses motion from change detectionmight be used here; the available algorithm options are vast and rapidlyexpanding, with open source projects such as open CV leading the charge.Numerous vendors sell high-resolution cameras, with excess of onemillion pixels, capable of more than one hundred frames per second. Withonly a few frames, bulk motion along the planned path of a vehicle canbe detected, so cueing can happen very fast. The first vector interrupt702 can be to the ladar system 206 itself. In this example embodiment,the ladar scheduler 704 is interrupted and over-ridden (step 706) with arequest to interrogate the object observed by the sensor at step 700. Inthis example, the laser is assumed to be bore-sited. The absence ofparallax allows rapid transfer of coordinates from camera to laser.Next, if needed, a command is issued to, and executed by, scanners 602and 604, to orient the laser to fire at the item of interest (step 708).A set of ladar shots is then fired (step 710) (for example, by ladartransmitter 302), and a return pulse is collected (step 712) in aphotodetector and analyzed (for example, by ladar receiver 304). Tofurther reduce latency, a selectable focal plane is employed so only thedesired cell of the received focal plane is interrogated (step 714) (forexample, via multiplexer 504). This streamlines read times in passingthe ladar data out of the optical detector and into digital memory foranalysis. When the ladar system 206 itself is the source of the originalcue, this can be referred to as self cueing, as opposed to cross cueingwhereby one sensor cues another.

Next a single range point pulse return (see 716) is digitized andanalyzed at step 718 to determine peak, first and last returns. Thisoperation can exploit the point cloud 352 to also consider prior one ormore range point pulse returns. This process is repeated as requireduntil the candidate threat object has been interrogated with sufficientfidelity to make a vector interrupt decision. Should an interrupt 720 bedeemed necessary, the motion planning system 202 is notified, whichresults in the pipelined queue 722 of motion planning system 202 beinginterrupted, and a new path plan (and associated necessary motion) canbe inserted at the top of the stack (step 724).

Taking 100 m as a nominal point of reference for our scenario, the timerequired from launch of pulse at step 710 to completion of range profile(see 716) is about 600 nsec since

$\frac{\frac{3}{2}10^{8}\mspace{14mu} m}{s} \times \frac{2}{3}10^{- 6}\mspace{14mu}{\left. s \right.\sim 100}\mspace{14mu}{m.}$

The lower portions of FIG. 7 show expected comparative timing anddistances (cumulatively) for each stage of this process with respect toan example embodiment disclosed herein and roughly analogous stages of aconventional raster scan system. The difference is striking, with theexample inventive embodiment being expect to we require less than 1 footof motion of the obstacle versus 68 feet for the conventional system.This portion of FIG. 7 includes stars to indicate stages that aredominant sources of latency.

It is instructive to explore the source of the latency savings at eachstage in the processing chain of FIG. 7.

The first stage of latency in the sensor processing chain is the timefrom when the cuing sensor receives the raw wavefront from a threat itemto when the ladar system controller determines the direction the ladartransmitter should point to address this threat item. Since it isexpected that we will need several frames from the cueing sensor todetect a threat, the time delay here is dominated by the frame time ofcueing sensor. Conventionally this involves nominally 20 Hz of updaterate, but can be reduced to 100 Hz with fast frame video, with a focuson regions presenting collision potential. For example, a 45-degree scanvolume is expected to require only about 30%-pixel inspection forcollision assessment. With embedded processors currently availableoperating in the 100 GOPs range (billions of operations per second), theimage analysis stage for anomaly detection can be ignored in countingexecution time, hence the camera collection time dominates.

The next stage of appreciable latency is scheduling a ladar shot throughan interrupt (e.g., via fast path ladar shot re-tasking). The latency inplacing a new shot on the top of the scheduling stack is dominated bythe minimum spatial repetition rate of the laser 402. Spatial repetitionis defined by the minimum time required to revisit a spot in the scene.For current conventional scanning ladar systems, this timeline is on theorder of 10 Hz, or 100 msec period. For an intelligent range point,e.g., scanning ladar with compressive sensing, the minimum spatialrepetition rate is dictated by the scan speed. This is limited by themechanical slew rate of the MEMS device (or the equivalent mechanicalhysteresis for thermally controlled electric scanning). 5 KHz is arather conservative estimate for the timelines required. This stageleaves us with the object now expected to have moved an additionaldistance of ˜13 feet with conventional approaches as compared to anexpected additional distance of less than 1 foot with the exampleinventive embodiment. The next step is to compute the commands to theladar transmitter and the execution of the setup time for the laser tofire. We deem this time to be small and comparable for both theconventional and inventive methods, being on the order of a 100 KHzrate. This is commensurate with the firing times of most automotiveladar systems.

The next stage of appreciable latency in the motion planning sensorpipeline is the firing of the ladar pulses and collection of returns.Since the time of flight is negligible (around 600 nanoseconds per theaforementioned analysis), this stage is dominated time-wise by therequired time between shots. Since multiple observations is expected tobe needed in order to build confidence in the ladar reporting, thisstage can become dominant. Indeed, for current lasers with spatialrevisit of 10 Hz, 5 shots (which is expected to be a minimum number ofshots needed to reliably use for safe interrupt) leads to ½ seconds oflatency. With a laser capable of dedicated gaze, the 5 shots can belaunched within the re-fire rate of the laser (where a re-fire time ofaround 200 u-sec can be used as a conservative number). After the pulsesare fired, the additional latency before exploitation is minor, and isdominated by the memory paging of the ladar returns. The exact timingdepends on the electronics used by the practitioner, but a typicalamount, for current SDRAM, is on the order of one microsecond. Finally,the exploitation stage is needed to translate the ladar and cameraimagery into a decision to interrupt the motion planner (and if so, whatinformation to pass to the planner). This stage can be made very shortwith an intelligent range point ladar system. For a conventionalpipelined ladar system the latency is expected to be on the order of afixed frame, nominally 10 Hz.

Finally, interference is a source of latency in ladar systems, as wellas radar and other active imagers (e.g., ultrasound). The reason is thatdata mining, machine learning, and inference may be used to ferret outsuch noise. To achieve low latency, motion planning can use in strideinterference mitigation. One such method is the use of pulse coding asdisclosed in U.S. patent application Ser. No. 62/460,520, filed Feb. 17,2017 and entitled “Method and System for Ladar Pulse Deconfliction”, theentire disclosure of which is incorporated herein by reference.Additional methods are proposed here. One source of sporadicinterference is saturation of the receiver due to either “own” ladarsystem-induced saturation from strong returns, or those of other ladarsystems. Such saturation can be overcome with a protection circuit whichprevents current spikes from entering the amplifiers in the ladarreceiver's photodetector circuit. Such a protection circuit can bemanufactured as a metallization layer than can be added or discardedselectively during manufacturing, depending on the practitioner's desireto trade sensitivity versus latency from saturation. Such a protectioncircuit can be designed to operate as follows: when the current spikeexceeds a certain value, a feedback circuit chokes the output; thisprotects the photodiode at the expense of reduced sensitivity (forexample, increased noise equivalent power). FIG. 9 shows an exampleembodiment of such a protection circuit where a protection diode 920 isused so that when the voltage 910 at the output of the firsttransimpedance amplifier (for example) is exceeded, the diode 920 isactivated. Upon activation of diode 920, current flows and energy isdiverted from the subsequent detection circuitry 930.

FIG. 8 discloses example process flows for collaborative detection ofvarious kinds of threats. The different columns in FIG. 8 showsdifferent types of threats that can be detected by threat detectionintelligence that is incorporated into an intelligent sensor. In thisexample, the types of threats that can be detected include a “swerve”800, a “shimmer” 810, and a “shiny object” 820. It should be understoodthat these threat types are examples only, and the threat detectionintelligence can also be configured to detect different and/oradditional threats if desired by a practitioner.

The rows of FIG. 8 indicate which elements or stages of the system canbe used to perform various operations in the collaborative model. Thefirst row corresponds to sensor cueing operations. The second rowcorresponds to point cloud exploitation by a ladar system or othersensor intelligence (such as camera intelligence). In general the signalprocessing for point cloud exploitation will be executed in an FPGA, orcustom processor, to keep the latency down to the level where the sensorcollection times, not the processing, are the limiting factors. Thethird row corresponds to interrupt operations performed by a motionplanning system 202.

Ladar self-cueing 802 can be used to detect a swerve event. With aswerve threat detection, the ladar system obtains ladar frame dataindicative of incoming vehicles within the lane of the ladar-equippedvehicle and/or incoming vehicles moving erratically. The ladar systemcan employ a raster scan across a region of interest. This region ofinterest may be, for example, the road that the ladar-equipped vehicleis centered on, viewed at the horizon, where incoming vehicles willfirst be detected. In this case, we might have a vehicle that, fromscan-to-scan exhibits erratic behavior. This might be (i) the vehicle isweaving in and out of lanes as evidenced by changes in the azimuth beamit is detected in, (ii) the vehicle is approaching from the wrong lane,perhaps because it is passing another vehicle, or (iii) the vehicle ismoving at a speed significantly above, or below, what road conditions,and signage, warrants as safe. All three of these conditions can beidentified in one or a few frames of data (see step 804).

At step 804, the point cloud 352 is routinely updated and posted to themotion planning system 202 via frame data 220. In background mode,threat detection intelligence within the ladar system 206 (e.g., anFPGA) tracks individual objects within the region. A nonlinear rangerate or angle rate can reveal if tracks for an object are erratic orindicative of lane-changing. If object motion is deemed threatening, aninterrupt can be issued to the motion planner (e.g., via priority flag250).

At step 806, the motion planner is informed via the interrupt that thereis incoming traffic that is a hazard due to a detected swerve condition(e.g., a threat of a head-to-head collision or simply an incomingvehicle-to-vehicle collision).

The example process flow for “shimmer” detection 810 can involvecross-cueing from another sensor as shown by step 812. Here, anembodiment is shown whereby a change is detected in a cluster of camerapixels, along the path of the ladar-equipped vehicle. This change mightbe due to shimmering leaves if the car is travelling through a forestedarea, or it could be due to a deer leaping onto the road. This cameradetection can then be used to cue a ladar system for additional probing.

A ladar system can sense motion within two shots at step 814, and with afew shots it can also determine the size of the moving object. Suchlearning would take much longer with a passive camera alone.Accordingly, when the camera detects a change indicative of a shimmer,this can cue the ladar system to target ladar shots toward theshimmering regions. Intelligence that processes the ladar returns cancreate blob motion models, and these blob motion models can be analyzedto determine whether a motion planning interrupt is warranted.

At step 816, the motion planner is informed via the interrupt that thereis an obstacle (which may have been absent from prior point cloudframes), and where this obstacle might be on a collision course with thevehicle.

A third example of a threat detection process flow is for a shiny object820, which can be another cross-cueing example (see 822). At step 824,an object is observed in a single camera frame, where the object has acolor not present in recent pre-existing frames. This is deemed unlikelyto have been obtained from the natural order, and hence is presumed tobe a human artifact. Such a color change can be flagged in a colorhistogram for the point cloud. A tasked ladar shot can quickly determinethe location of this object and determine if it is a small piece ofdebris or part of a moving, and potential threatening, object via acomparison to the vehicle's motion path. An interrupt can be issued ifthe color change object is deemed threatening (whereupon step 816 can beperformed).

In the example of FIG. 8, it is expected that the compute complexitywill be low—on the order of a few dozen operations per shot, which theinventors believe to be amenable to low latency solutions.

The examples of FIGS. 10A-10D show how co-bore siting a camera with theladar receiver can improve the latency by which ladar data is processed.In conventional laser systems, a camera is positioned exterior to thelaser system. This arrangement requires a computationally-intensive (andtherefore latency-inducing) task in order to re-align the camera imagewith the laser. This re-alignment process for conventional laser systemswith exterior cameras is known as parallax removal. To avoid suchparallax removal tasks, FIGS. 10A-10D describe an example embodimentwhere a camera and ladar transceiver are part of a common opticalengine. For example, FIGS. 10A-C show an example where a camera 1002 isco-bore sited with the photodetector 500 of a ladar receiver. Camera1002 can be a video camera, although this need not be the case. Theexample of FIGS. 10A-C are similar to the example of FIGS. 6A-C with theexception of the co-bore sited camera 1002.

The lens 610 separates the receiver from the exterior environment and isconfigured to that it receives both visible and laser band light. Toachieve co-bore siting, the optical system includes a mirror 1000 thatis positioned optically between the lens 610 and photodetector 500 aswell as optically between the lens 610 and camera 1002. The mirror 1000,photodetector 500 and camera 1002 can be commonly housed in the sameenclosure or housing as part of an integrated ladar system. Mirror 1000can be a dichroic mirror, so that its reflective properties vary basedon the frequency or wavelength of the incident light. In an exampleembodiment, the dichroic mirror 1000 is configured to (1) directincident light from the lens 610 in a first light spectrum (e.g., avisible light spectrum, an infrared (IR) light spectrum, etc.) to thecamera 1002 via path 1004 and (2) direct incident light from the lens610 in a second light spectrum (e.g., a laser light spectrum that wouldinclude ladar pulse reflections) to the photodetector 500 via path 1006.For example, the mirror 1000 can reflect light in the first lightspectrum toward the camera 1002 (see path 1004) and pass light in thesecond light spectrum toward the photodetector 500 (see path 1006).Because the photodetector 500 and camera 1002 will share the same fieldof view due to the co-bore siting, this greatly streamlines the fusionof image data from the camera 1002 with range point data derived fromthe photodetector 500, particularly in stereoscopic systems. That is,the image data from the camera 1002 can be spatially aligned withcomputed range point data derived from the photodetector 500 withoutrequiring the computationally-intensive parallax removal tasks that areneeded by conventional systems in the art. For example, a typical highframe rate stereoscopic video stream requires 10 s of Gigaflops ofprocessing to align the video to the ladar data, notwithstanding lossesin acuity from registration errors. These can be avoided using theco-bore sited camera 1002. Instead of employing Gigaflops of processingto align video and ladar, the use of the co-bore sited camera can allowfor alignment using less complicated techniques. For example, tocalibrate, at a factory assembly station, one can use a ladar system anda camera to capture an image of a checkboard pattern. Anyinconsistencies between the camera image and the ladar image can then beobserved, and these inconsistencies can then be removed by hardwiring analignment of the readout code. It is expected that for commercial-gradeladar systems and cameras, these inconsistencies will be sparse. Forexample, suppose both camera and ladar system have an x-y pixel grid of100×100 pixels. Then, both the ladar system and camera image against a100×100 black and white checker board. In this example, the result maybe that the pixels all line up except in upper right corner pixel100,100 of the camera image is pointed off the grid, and pixel 99,100 ofthe camera image is at checkerboard edge, while the ladar image has bothpixels 99,100 and 100,100 pointing at the corner. The alignment is thensimply as follows:

-   -   1) Define the camera pixels in x and y respectively as i and j,        with range being i,j=1, . . . , 100, and ladar as k, and 1,        again with k,l=1, . . . , 100.    -   2) Index (fuse/align) the ladar based on pixel registrations        with camera image. For example, suppose a camera pixel, say,        12,23 is inspected. Now suppose we want to likewise inspect its        ladar counterpart. To do so, the system recalls (e.g., fetches        from memory) pixel 12,23 in the ladar data. With respect to the        example above, if the camera pixel is any pixel other than        99,100 or 100,100; then the recalled ladar pixel is the same as        the camera pixel; and if we are accessing pixel 99,100 in the        camera, we select an aggregation of pixels 99,100 and 100,100 in        the ladar image; and if the camera image is at pixel 100,100, we        access no ladar image.    -   3) Repeat in similar, though reversed, direction for ladar-cued        camera.        Note that no complex operations are required to perform this        alignment. Instead, a simple, small, logic table is all that is        needed for each data query, typically a few kB. In contrast many        Gbytes are required for non-bore sited alignment.

FIG. 10D depicts an example process flow showing how the co-bore sitedcamera 1002 can be advantageously used in a system. At step 1050, lightis received. This received light may include one or more ladar pulsereflections as discussed above. Step 1050 can be performed by lens 610.At step 1052, portions of the received light in the first light spectrumare directed toward the camera 1002, and portions of the received lightin the second light spectrum are directed toward the photodetector 500.As noted above, the second light spectrum encompasses the spectrum forladar pulses and ladar pulse reflections. This step can be performed bymirror 1000.

At step 1054, the photodetector 1054 detects the ladar pulse reflectionsdirected to it by the mirror 1000. At step 1056, range point data iscomputed based on the detected ladar pulse reflections. Step 1056 can beperformed using a signal processing circuit and processor as discussedabove.

Meanwhile, at step 1058, the camera 1002 generates image data based onthe light directed to it by the mirror 1000. Thereafter, a processor canspatially align the computed range point data from step 1056 with theimage data from step 1058 (see step 1060). Next, the ladar system and/ormotion planning system can make decisions about ladar targeting and/orvehicle motion based on the spatially-aligned range point and imagedata. For example, as shown by FIGS. 10B-C, this decision-making canresult in the insertion of new shots in shot list 400. Further still,the motion planning system 202 can choose to alter vehicle motion insome fashion based on the content of the spatially-aligned range pointand image data.

It should be understood that FIGS. 10A-10C show example embodiments, anda practitioner may choose to include more optical elements in thesystem. For example, additional optical elements may be included in theoptical paths after splitting by mirror 1000, such as in the opticalpath 1004 between the mirror 1000 and camera 1002 and/or in the opticalpath 1006 between the mirror 1000 and photodetector 500. Furthermore,the wavelength for camera 1002 can be a visible color spectrum, a greyscale spectrum, a passive IR spectrum, hyperspectral spectrum, and withor without zoom magnification. Also, the focal plane of the ladar systemmight have sufficient acceptance wavelength to serve as a combinedactive (ladar) and passive (video) focal plane.

Another advantage of latency reduction is the ability to compute motiondata about an object based on data within a single frame of ladar data.For example, true estimates of (3D) velocity and acceleration can becomputed from the ladar data within a single frame of ladar shots. Thisis due to the short pulse duration associated with fiber or diode ladarsystems, which allows for multiple target measurements within a shorttimeline. Velocity estimation allows for the removal of motionlessobjects (which will have closing velocity if a ladar-equipped vehicle isin motion). Velocity estimation also allows for a reduction in theamount of noise that is present when a track is initiated afterdetection has occurred. For example, without velocity, at 100 m and a 10mrad beam, one might require a range association window of 3 m, whichfor a 3 ns pulse corresponds to 18 x,y resolution bins of noise exposure(½m from pulse width, and 1 m from beam divergence). In contrast, ifthere is a velocity filter of 3 m/s in both dimensions in addition tothe 3 m association, then, at a nominal 10 Hz frame rate, the extent ofnoise exposure reduces to around 2-4 bins. The ability to compute motiondata about an object on an intraframe basis allows for robust kinematicmodels of the objects to be created at low latency.

FIG. 11A shows an example process flow for computing intra-frame motiondata in accordance with an example embodiment. At step 1100, the ladartransmitter 302 fires a cluster of overlapping ladar pulse shots at atarget within a single ladar frame. The ladar pulses in the cluster arespaced apart in time over a short duration (e.g., around 5 microsecondsto around 80 microseconds for typical MEMS resonance speeds inembodiments where MEMS scanning mirrors are employed by the ladarsystem). The beam cluster can provide overlap in all threedimensions—azimuth, elevation, and range. This can be visualized bynoting that each ladar pulse carves out, over the flight time of thepulse, a cone of light. At any point in time, one can calculate from themirror positions where this cone will be positioned in space. Thisinformation can be used to select pulse shot times in the scheduler toensure overlap in all three dimensions. This overlap provides a uniquesource of information about the scene by effectively using differentlook angles (parallax) to extract information about the scene.

The latency advantage of this clustering approach to motion estimationcan be magnified when used in combination with a dynamic ladar systemthat employs compressive sensing as described in the above-referencedand incorporated patents and patent applications. With such a dynamicladar system, the ladar controller exerts influence on the ladartransmissions at a per pulse (i.e. per shot) basis. By contrast,conventional ladar systems define a fixed frame that starts and stopswhen the shot pattern repeats. That is, the shot pattern within a frameis fixed at the start of the frame and is not dynamically adapted withinthe frame. With a dynamic ladar system that employs compressive sensing,the shot pattern can dynamically vary within a frame (i.e., intraframedynamism)—that is, the shot pattern for the i-th shot can depend onimmediate results of shot i−1.

Typical fixed frame ladar systems have frames that are defined by theFOV; the FOV is scanned shot to shot; and when the FOV has been fullyinterrogated, the process repeats. Accordingly, while the ability of adynamic ladar system that employs compressive sensing to adapt its shotselection is measured in microseconds; the ability of conventional fixedframe ladar systems to adapt its shot selection is measured in hundredsof milliseconds; or 100,000× slower. Thus, the ability the of usedynamically-selected tight clusters of intraframe ladar pulses to helpestimate motion data is expected to yield significantly improvements inlatency with respect to object motion estimation.

Returning to FIG. 11A, meanwhile, at step 1102, the ladar receiver 304receives and processes the reflection returns from the cluster of ladarpulses. As part of this processing, the ladar receiver 304 can computeintraframe motion data for the target. This motion data can be computedbased on changes in range and intensity with respect to the reflectionreturns from the range points targeted by the tight cluster. Forexample, velocity and acceleration for the target can be estimated basedon these reflection returns. By computing such motion data on anintra-frame basis, significant reductions in latency can be achievedwith respect to modeling the motion of one or more targets in the fieldof view, which in turn translates to faster decision-making by a motionplanning system.

FIG. 11B shows an example process flow for implementing steps 1100 and1102 from FIG. 11A. FIG. 12A shows an example cluster of ladar pulsebeams for reference with respect to FIG. 11B. FIG. 11B begins with step1110, where a target of interest is detected. Any of a number oftechniques can be used to perform target detection. For example, ladardata and/or video data can be processed at step 1110 to detect a target.Further still, software-defined frames (examples of which are discussedbelow) can be processed to detect a target of interest. As an example, arandom frame can be selected at step 1110, the target can be declared asthe returns whose range does not map to fixed points from a highresolution map. However, it should be understood that other techniquesfor target detection can be employed.

At step 1112, the coordinates of the detected target can be defined withrespect to two axes that are orthogonal to each other. For ease ofreference, these coordinates can be referred to as X and Y. In anexample embodiment, X can refer to a coordinate along a horizontal(azimuth) axis, and Y can refer to a coordinate along a vertical(elevation) axis.

At step 1114, the ladar transmitter 302 fires ladar shots B and C at thetarget, where ladar shots B and C share the same horizontal coordinate Xbut have different vertical coordinates such that ladar shots B and Chave overlapping beams at the specified distance in the field of view.FIG. 12A shows an example of possible placements for ladar shots B andC. It should be understood that the radii of the beams will be dependenton both optics (divergence) and range to target.

At step 1116, the ladar receiver 304 receives and processes returns fromladar shots B and C. These reflection returns are processed to computean estimated target elevation, Yt. To do so, the shot energy of thereturns from B and C can be compared. For example, if the energy for thetwo returns is equal, the target can be deemed to exist at the midpointof the line between the centers of B and C. If the energy in the Breturn exceeds the energy in the C return, then the target can be deemedto exist above this midpoint by an amount corresponding to the energyratio of the B and C returns (e.g., proportionally closer to the Bcenter for corresponding greater energy in the B return relative to theC return). If the energy in the C return exceeds the energy in the Breturn, then the target can be deemed to exist below this midpoint by anamount corresponding to the energy ratio of the B and C returns (e.g.,proportionally closer to the C center for corresponding greater energyin the C return relative to the B return).

At step 1118, a new ladar shot B′ is defined to target a range point atvertical coordinate Yt and horizontal coordinate X. Next, at step 1120,a new ladar shot A is defined to target a range point at verticalcoordinate Yt and at a horizontal coordinate X′, where the offsetbetween X and X′ is large enough to allow estimation of cross-rangetarget position but small enough to avoid missing the target with eitherB′ or A. That is, at least one of B′ and A will hit the detected target.The choice of X′ will depend on the distance to, and dimensions of,objects that are being characterized as well as the ladar beamdivergence, using mathematics. For example, with a 10 mrad beam, 100 mrange, and a 1 m width vehicle (e.g., motorbike), when viewed from therear, the value for X′ can be defined to be ½ meter.

At step 1122, the ladar transmitter 302 fires ladar shots B′ and A attheir respective targeted range points. The ladar receiver 304 thenreceives and processes the reflection returns for B′ and A to computerange and intensity data for B′ and A (step 1124). Specifically, thedesired reflection values can be obtained by taking the standard ladarrange equation, inputting fixed ladar system parameters, and calculatingwhat the target reflectivity must have been to achieve the measuredsignal pulse energy. The range can be evaluated using time of flighttechniques. The ranges can be denoted by Range(B′) and Range(A). Theintensities can be denoted by Intensity(B′) and Intensity(A). Then, atstep 1126, a processor computes the cross-range and range centroid ofthe target based on Range(B′), Range(A), Intensity(B′), andIntensity(A). The cross-range can be found by computing the range (timeof flight), denoted by r, azimuth angle θ (from shot fire time andmirror position), and evaluating the polar conversion r sin(θ). WithI(i) denoting intensity and with multiple measurements the rangecentroid is found as:

$\sum\limits_{i}\frac{{I(i)}{r(i)}}{\sum_{k}{I(k)}}$while the cross range centroid is

$\sum\limits_{i}\frac{{I(i)}{r(i)}{\sin\left( \theta_{i} \right)}}{\sum_{k}{I(k)}}$As new data is collected after a period time long enough to allow theobject of interest to move by at least a few millimeters, orcentimeters, this process can be repeated from scratch, and the changesin position for the centroid can be used to estimate velocity andacceleration for the target.

FIG. 12A shows an example beam layout in a field of view for a ladarshot cluster that helps illustrate the principle of operation withrespect to FIG. 11B. Shown by FIG. 12A are beam positions A, B, C (1200)where a target is interrogated. These beams are overlapping at the fullwidth half maximum, or 1/e² level. For the purpose of discussion and thesake of pedagogic demonstration, it will be assumed that (i) the validtarget lies within the union of these beams A, B, and C, and (ii) A, Band B, C are coplanar. In this context, three beams are defined to becoplanar if there exists a pairwise paring whose joint collinear lookdirections are orthogonal. In FIG. 12A, it can be seen that this is thecase because A,B align horizontally and B,C align vertically. Note thatwe do not require that the central axis (phase centers) be coincident.The true target lies at 1201 within the field of view. As noted, theprocess flow of FIG. 11B can allow an interpolation in azimuth andelevation to obtain a refined estimate of target location at a point intime. A ladar beam centered on (or near) this refined estimate can bedenoted as A′ (1202). In practice, A′ will rarely if ever be perfectlycentered on 1201, because (i) we are only approximating the true valueof 1201 with our centroid due to noise, and (ii) the software willgenerally, if not always, be limited to a quantized set of selectablepositions. Once beam A′ is created, the system can also create collinearoverlapping ladar beams B′ and C′ as well (not shown in FIG. 12A forease of illustration). The result of interrogations and analysis viathese beams will be the velocity table shown by FIG. 12B. From this, thesystem can produce refined estimates of range and angular position forthe target, but for purposes of discussion it suffices to considerknowledge as a substantially reduced fraction of beam divergence (angleposition), the range case follows similarly. This accuracy allows for alook at pairwise changes in angle and range to extract velocity andacceleration information for the target. Accordingly, the process flowof FIG. 11B can be used to track lateral motion for targets.

FIG. 12B shows example timelines for target tracking in accordance withthe example process flow of FIG. 11B, and these timelines show theclustering technique for computing intraframe motion data to be superiorto conventional approaches as discussed below. Moreover, the timelinesare short enough to not hinder the speed of the scan mirrors, which canbe helpful for maintaining a large field of view while also achieved lowmotion planning latency.

To provide a sample realistic scenario, and velocity extractioncomparison with existing coherent FMCW (Frequency Modulation ContinuousWave) lasers, we assume a 3 ns pulse, a 100 m target, 25 meter/secondclosing target speed, and 10% non-radial speed for the overall velocityvector. We also assume commensurate ratios for acceleration, with 1/ms²acceleration (roughly 10% of standard gravity g-force). We assume anability to measure down to 20% of the uncertainty in beam and range bin,as defined at the full width half maximum (FWHM) level. We use a 15 KHzfast scan axis assumption, which in turn leads to nominal 30 μsecrevisit rate. As a basis of comparison, we can consider the FMCW laserdisclosed in U.S. Pat. Nos. 9,735,885 and 9,310,471, which describe aladar system based on Doppler extraction and has a dwell time of no lessthan 500 nanoseconds. This has a disadvantage in that the beam will slewa lot during that time, which can be overcome with photonic steering;but the need for high duty cycle and time integration for Dopplerexcision limit the achievable pulse narrowness. The current comparisonfor FIG. 12B is based on the ability to extract non-radial velocity.

In FIG. 12B, we see that the time between shots for position accuracyrefinement is between 30 μsec and 2,000 μsec (shown as 2 ms in FIG.12B). The upper limit is the time before the range has drifted such thatwe are conflating range with range rate. To estimate range rate, we waituntil the target has moved by an amount that can be reliably estimated.That would be about 3 mm (for a 10 cm duration [˜3.3 ns], SNR-60). So,at 25 m/s, we can detect motion in increments of 20% for motion of 5m/s, which with a 1 ms update translates to 5 mm. We conclude that 1 m/sis a good lower bound on the range rate update as reflected in FIG. 12B.For angular target slew, the acceleration is not discernable, andtherefore velocity is not blurred, for motion of 1 m/s/s/5, or 20cm/s/s. For 10% offset this becomes 2 cm/s. At 100 m with 3 mrad offset,we obtain ˜30 cm cross-range extent, or 300 mm, becoming, after 5:1split, 60 k μm. Hence, in 1 ms of time, acceleration motion, with 5:1splitting is 20 μm. To become 5:1 of our angular beam, we then grow thisby 3,000. We conclude that the limiting factor for dwell in angle spaceis velocity beam walk not acceleration. Here, we see that we walk ⅕^(th)of a beam with 6 cm, so at our specified 10% of 25 m/s crab, we get 2.5m/s, or 2.5 mm/ms. We get blurring then around about 10 ms. We provide amargin between upper range bounds and lower range rate bounds of anominal 50%. We do not include acceleration in FIG. 12B, becausereliable position gradient invariably involves terrain and kinematicconstraints making more complex radiometric and ego motion modelingnecessary. Software tools are available from Mathworks, MathCAD, Ansys,and others can assist in this endeavor.

Acceleration typically blurs velocity by 5 mm/s in 5 ms. To detect 5m/s, our specification from the above paragraph, we would have 10×margin with this rate of blur. Since error accumulates with eachsuccessive differential operator, it is prudent to take such margin, andhence we use 5 ms as the maximum duration between samples for truevelocity. Side information can expand this substantially, by nowherenear the 10-20 Hz common for spinning systems.

In FIG. 12B, angular beam positions at time of launch (shot list) aredenoted by capital letters, and time of pulse launch in lower case. Weshow examples of data pairings used to obtain range and range rate inall three (polar) coordinates. Mapping to standard Euclidean unitvectors is then straight forward.

While the ability to obtain with a single frame (intraframe) improvedranging, velocity, and acceleration, leads to improved ladar metricssuch as effective SNR and mensuration, it should be understood thatthere are additional benefits which accrue when this capability iscombined with intraframe communications such as communications withimage data derived from a camera or the like, as discussed below.

Once communication between the ladar system and a camera is establishedinter-frame, still more latency reduction can be achieved. To facilitateadditional latency reduction, an example embodiment employssoftware-defined frames (SDFs). An SDF is a shot list for a ladar framethat identifies a set, or family, of pre-specified laser shot patternsthat can be selected on a frame-by-frame basis for the ladar system. Theselection process can be aided by data about the field of view, such asa perception stack accessible to the motion planning system and/or ladarsystem, or by the end user. Processing intelligence in the ladar systemcan aid in the selection of SDFs or the selection can be based onmachine learning, frame data from a camera, or even from map data.

For example, if an incoming vehicle is in the midst of passing a car infront of it, and thereby heading at speed towards a (potential) head oncollision, the SDF stack can select an area of interest around theingressing threat vehicle for closer monitoring. Extending on this,multiple areas of interest can be established around various vehicles,or fixed objects for precision localization using motion structure.These areas of interest for interrogation via ladar pulses can bedefined by the SDFs; and as examples the SDFs can be a fixed grid, arandom grid, or a “foviated” grid, with a dense shot pattern around adesired “fixation point” and more sparsely sampled elsewhere. In caseswhere a potential collision is predicted, the ladar frame can beterminated and reset. There can be a great benefit to leverageladar/camera vision software that emulates existing fixed ladar systems,such as spinning lidars. Accordingly, there is value to the practitionerin including, in the SDF suite, emulation modes for fixed ladar scannersin order to leverage such software. For example, a foviated SDF can beconfigured whereby the road segment that the ladar-equipped vehicle isentering enjoys a higher shot density, possibly with highest density atthe detection horizon and/or geographic horizon, whereby the sky, andother non-road segments are more sparsely populated with shots.

FIG. 13A discloses an example process flow for execution by a processorto select SDFs for ladar system on the basis of observed characteristicsin the field of view for the ladar system. This permits low latencyadaptation of ladar shots on a frame-by-frame basis.

At step 1300, the processor checks whether a new ladar frame is to bestarted. If so, the process flow proceeds to step 1302. At step 1302,the processor processes data that is representative of one or morecharacteristics of the field of view for the ladar system. Thisprocessed data can include one or more of ladar data (e.g., rangeinformation from prior ladar shots), image data (e.g., images from avideo camera or the like), and/or map data (e.g., data about knownobjects in or near a road according to existing map information). Basedon this processed data, the processor can make judgments about the fieldof view and select an appropriate SDF from among a library of SDFs thatis appropriate for the observed characteristics of the field of view(step 1304). The library of SDFs can be stored in memory accessible tothe processor, and each SDF can include one or more parameters thatallow for the specific ladar shots of that SDF to be further tailored tobest fit the observed situation. For example, these parameters caninclude one or more variables that control at least one of spacingbetween ladar pulses of the SDF, patterns defined by the ladar pulses ofthe SDF (e.g, where the horizon or other feature of a foviated SDF islocated, as discussed below), and specific coordinates for targeting byladar pulses of the SDF. At step 1306, the processor instantiates theselected SDF based on its parameterization. This results in the SDFspecifically identifying a plurality of range points for targeting withladar pulses in a given ladar frame. At step 1308, the ladar transmitterthen fires ladar pulses in accordance with the instantiated SDF. Uponcompletion of the SDF (or possibly in response to an interrupt of thesubject ladar frame), the process flow returns to step 1300 for the nextframe.

FIGS. 13B-13I show examples of different types of SDFs that can employedby the ladar system as part of the example embodiment of FIG. 13A, wherethe various lines and other patterns in the examples of FIGS. 13B-13Ishow where ladar pulses are to be targeted in the field of view.

FIG. 13B shows an example raster emulation SDF. The raster patterndefined by this SDF corresponds to the standard scan pattern used bymany ladar systems. To maintain agility, it is desirable for the ladarsystem to emulate any existing ladar system, which allows the ladarsystem to leverage existing ladar exploitation software.

FIGS. 13C-13D show examples of foviation SDFs. With a foviation SDF, theladar shots are clustered in areas that are deemed a potential threatarea or other region of interest to allow for fast threat response. Anexample of a potential threat area in a field of view would be the lanetraveled by the ladar-equipped vehicle. The foviation can vary based ona number of patterns. For example, FIG. 13C shows an elevation (orvertical) foviation SDF. Note that foviation is defined as the axiswhere sparsification (lower and higher density) is desired. This isopposite the axis of symmetry, i.e. the axis where shots are (uniformly)densely applied. In the example of FIG. 13C, the foviation focuses on aparticular elevation in the field of view, but scans all the horizon. Adesirable elevation choice is the intersection of the earth horizon andthe lane in which the ladar-equipped vehicle is traveling. We might alsowant to scan the lane horizontally, and beyond, to see if there is anupcoming intersection with vehicle ingress or egress. In elevation, theshot density is highest at this horizon area and lower at immediatelyhigher or lower elevations or perhaps lower at all other elevations,depending on the degree of sparsification desired. Another potentialfoviation pattern is an azimuth (or horizontal) foviation SDF, in whichcase the higher density of shots would correspond to a vertical line atsome define position along the horizontal axis (azimuth is sparse andsymmetry is vertical). Another example is a centroidal foviation SDF, anexample of which is shown by FIG. 13D. In the example of FIG. 13D, thefoviation is focused along a specified vertical and horizontalcoordinate which leads to a centroidal high density of ladar shots atthis elevation/azimuth intersection. Parameterization of the foviationSDFs can define the locations for these high densities of ladar shots(as well as the density of such shots within the SDF).

FIG. 13E shows an example of a random SDF, where the ladar shots arespread throughout the field of view based on a random sampling oflocations. The random SDF can help support fast threat detection byenabling ambiguity suppression. A random SDF can be parameterized tocontrol the degree of randomness and the spacing of random shots withinthe field of view. For example, the random list of ladar shots in therandom SDF can be controllably defined so that no potential target canmove more than 10 feet before being detected by the ladar system.

FIG. 13F shows an example of a region of interest SDF. As an example, aregion of interest SDF can define regions of interest for targeting withladar shots within a given ladar frame. Examples shown by FIG. 13Finclude fixed regions such as tripwires where vehicles might ingress oregress (see the thin lines at road crossings in FIG. 13F)) and/orbounding boxes (see the rectangular box behind the car in the left handlane of FIG. 13F). This example shows a region of interest which isperhaps in front of the ladar system, an area of keen interest. Thebounding boxes can be obtained from prior ladar scans and/or machinevision (e.g., via processing of camera image data), and the boundingboxes enable the system to retain custody of previously detectedtargets. Examples of such targets can include pedestrian(s) detected viamachine vision, motorcycle(s) detected via machine vision, and streetsign(s) (or other fixed fiducials) detected via machine vision.

FIG. 13G shows an example image-cued SDF. The image that cues such anSDF can be a passive camera image or an image rendered from ladar data.As an example, the image-cued SDF can be based on edges that areobserved in the images, where detected edges can be used to form a framefor subsequent query by the ladar system. As another example, theimage-cued SDF can be based on shadows that are observed in the images,where detected shadows can be used to form a frame for subsequent queryby the ladar system. Another example would be cueing of the ladar frombounding boxes to enhance the depth sensing of video-only systems. Forexample, dedicated range point selection as disclosed herein canleverage the technology described in “Estimating Depth from RGB andSparse Sensing”, by Magic Leap, published in Arxiv, 2018 toward thisend.

FIG. 13H shows an example map-cued SDF. A map-cued SDF can be a powerfulmode of operation for an agile ladar system because the frames are basedon own-car sensing but on previously-collected map data. For example,the map data can be used to detect a cross road, and a map-cued SDF canoperate to query the cross-road with ladar shots prior to arrival at thecross road. The cross road locations are shown in FIG. 13H as block dotswhile the white dot corresponds to a ladar-equipped vehicle entering anintersection.

Using single frame cueing assists both the ladar data and the cameradata. The camera can direct frame selection from the SDF suite. Also,the ladar data can be used to enhance video machine learning, byestablishing higher confidence when two objects are ambiguouslyassociated (resulting in an undesired, high, confusion matrix score).This example shows how existing software can be readily adapted andenhanced through intraframe (i.e. subframe) video/ladar fusion. Forexample, a standard spinning ladar system might have a frame rate of 10Hz. Many cameras allow frame rates around 100 Hz, while conventionalladar frames rates are generally around 10 Hz- or 10 times less than thecamera frame rates. Thus, by waiting for a ladar frame to be completebefore fusing camera data, it can be seen that there is latency involvedwhile the system waits for completion of the ladar frame. Accordingly,it should be understood that such sub frame processing speeds uplatency, and enables updating SDFs on a frame-to-frame basis withoutsetup time or interframe latency.

A particularly compelling use case of camera-ladar cross-cueing is shownin FIG. 13I. The left hand side of FIG. 13I shows a ladar map of a fieldof view, and the right hand side of FIG. 13I shows a camera image of thefield of view overlaid with this ladar map (where this example wasproduced using the AEYE JO-G ladar unit. It can be seen from the righthand side of FIG. 13I that the vehicles have moved from when the ladarmap was formed, and this can be detected by inspecting the visual edgesin the color camera image and comparing to the edges in the ladar image.This type of cross-cueing is very fast, and far less error prone thanattempting to connect edges from frame-to-frame alone in either cameraor ladar images. The time required to determine vehicle direction andspeed, after the ladar frame is finished is simply the time to take onecamera image (10 msec or so), followed by edge detection in the cameraimage (which can be performed using freely available Open CV software),which takes time on the order of 100K operations, which is a significantimprovement compared to the Giga-operations that would be expected foroptical camera images only.

FIG. 14 shows an example embodiment that elaborates on details ofseveral of the frames discussed above. First, considering shadowing inthe video camera, reference number 10017 shows an illumination source(e.g., the sun, street light, headlight, etc.), and reference number10018 shows an obstacle which occludes the light, thereby casting ashadow with boundaries defined by rays 10019 and 10020. In this example,all of target 10004 and some of target 10003 are not visible to acamera, but most of the shadowed region is recoverable using ladars10001 and 10002 via use of a shadow-cued SDF.

We observe a scenario where a vehicle 10003 is obstructing a secondvehicle 10004 that is more distant. For example, this may correspond toa scenario where the vehicle 10004 is a motorcycle that is in theprocess of overtaking vehicle 10003. This is a hazardous situation sincemany accidents involve motorcycles even though there are few motorcycleson the roads. In this example, we will consider that the motorcycle willbe, for some period of time, in the lane of the ladar-equipped vehicle10008, at least for some period of time. In our scenario, the vehicle10008 has two sensors heads, each which comprise ladar and video (10001and 10002). The shadow-cued SDF will be different for each ladar head.Note that the FIG. 14 sketch is not to scale, the separation betweensensors on the vehicle 10008 are generally two meters or so, and thedistance from vehicle 10008 to vehicle 10004 might be 100's of meters.Also the aspect ratio of the vehicles is not correct, but the formfactor simplifies the narrative and evinces the salient points moreeasily.

The two triangles with vertexes at 10001 and 10002, and encompassingrespectively shaded areas 10005, 10007 and 10006, 10007 show the fieldof view of the ladar and co-bore sited camera. Sensor 10001 has a shadowthat is cast from 10003 that is shown as the shaded area 10006. Notethat this includes vehicle 10004 so neither ladar nor camera see vehicle10004. The same is true of sensor 10002. Note the shadow from sensor10002 is cast from the rear of the vehicle and for sensor 10001 it iscast from the front on the right hand side, and on the left the front of10003 is the apex of both shadows. The structured textured region 10005shows the shadow of sensor 10002 which is not already shadowed fromsensor 10001. The stereoscopic structure from motion will produce abounding box for sensors 10001, 10002, where such bounding boxes areidentified by 10011, 10012 respectively on vehicle 10003; which definesa new (region of interest) software frame 10013 for each ladar, to trackvehicle 10003. The use of a tight ROI frame allows for lower SNR, for agiven net false alarm rate, and hence less recharge time, reducinglatency.

Note that if vehicle 10003 was not present, both cameras would seevehicle 10004, but from different angles. The practitioner will notethat this enables a video camera to obtain structure from motion,specifically to infer range from the angle differences. The ladars, ofcourse, give range directly. Since noise reduction arises from averagingthe outputs of sensors, it can be observed that when objects are notoccluded we can obtain more precise localization, and therefore speedand motion prediction, thereby again furthering range. This is shown inFIG. 14 by reference numbers 10014, 10015 [see the black dots] beinglocations from structured motion where the front of vehicle 10003 isestimated to be positioned. With the use of ranging from one or bothladars, we can obtain precise range, replacing the different, and bothslightly erroneous target positions 10014), 10015, with the moreaccurate position 10016 [see the white dot]. The improved positioning,using the multi-lateration from distance d, 10009, and angle offset a10010 leads immediately to a smaller estimated target volume 10013;which in turn increases effective SNR through reduced noise exposure asdiscussed previously in the velocity estimation narrative.

Itemized below are example use cases where motion planning value can beextracted from kinematic models such as 3D velocity and acceleration. Inthese examples, the detected and tracked target can be other vehiclesthat are within sensor range of the ladar-equipped vehicle. The motiondata for these other vehicles are then used to calculate anticipatedbehavior for the ladar-equipped vehicle, individually or in theaggregate (such as traffic density) or to extract environmentalconditions. These examples demonstrate the power of 3D velocity andacceleration estimation for use with the short pulse velocity ladardescribed herein. Furthermore, while these use cases are addressable byany ladar system, coherent or not, a coherent, random access ladarsystem as described herein and in the above-referenced and incorporatedpatents and patent applications is well-suited to the dynamic revisittimes and selective probing (e.g., variable shot list), as outlined inFIGS. 12A and 12B. Furthermore, all these use cases are enhanced if thecamera is able to feedback information to the ladar sensor faster than avideo frame rate. For example, we discussed earlier that the ladarsystem can sample a given position with 60 usec or less, whereas a videoframe is usually on the order of 5-10 msec (for a 200-100 fps camera),often at the sacrifice of FOV. As a result, if the camera can presentdata back to the ladar system on a per line basis; this can appreciablyreduce latency. For example, a camera with 3 k×3 k pixels, a modernstandard, would provide, with per line readout, a latency reduced by3,000. So even if the update rate at the full 9M-pixel was limited to 20Hz, we can have matched the ladar revisit if we drop the readout from aframe to a line. Because video exploitation is a more developed fieldthan ladar, there are a variety of software tools and productsavailable, such as Open CV, to address the below scenarios.

Environmental Perception for fast motion planning: exploiting othervehicles as “environmental probes”

-   -   Impending Bump/pothole—velocity redirected up or down along the        vertical y axis        -   Detection: rapid instantaneous vertical velocity, well in            excess of the gradient of the road as evinced from maps.        -   Utility: alert that a bump is imminent, allowing time to            decelerate for passenger comfort, and or enhanced vehicle            control.    -   Onset of Rain/ice (skidding)—microscale vehicle velocity shifts        in horizontal plane without bulk velocity vector change        -   Detection: random walk of position of vehicle returns well            in excess of standard ego (i.e., own car) motion.        -   Utility: alert of unsafe [reduced grip] road conditions,            update turn radius and braking models for vehicular control.    -   Impending Winding/curving road—gradual lateral change (swaying)        in horizontal direction with no change in speed, or possibly        reduced speed        -   Detection: Minor change in radially projected length of            vehicle, and/or difference in velocity of rear and front of            vehicle [if range resolved] or change in azimuthal centroid            when SNR dependent range resolution is insufficient for the            above detection modalities.        -   Utility: Lateral change provides advanced warning of            upcoming road curvature, allowing corrective action to            vehicle controls before own-car road surface changes.    -   Impending Traffic jam—deceleration/braking        -   Detection: Coherent Deceleration pattern of vehicles in            advance of lidar equipped vehicle.        -   Utility: Advanced warning of the need to slow down, and/or            change route planning exploring preferred path options.            Behavioral Perception: ascertaining intent of human or            robotic drivers. Perception of behavior is a matter of            anomalous detection. So, for example if a single vehicle is            “flip flopping” then road surface cannot be the probative            cause, rather the driving is decidedly the proximate            culprit.    -   Aberrant driver behavior [drunkard or malfunctioning        equipment]—subtle lateral change (swerving) in direction and        speed (velocity vector)        -   Detection: Change in radially projected length of vehicle,            and/or difference in velocity of rear and front of vehicle            [if range resolved] or change in azimuthal centroid when SNR            dependent range resolution is insufficient for the above            detection modalities.        -   Utility: An aberrant driver, robotic or human, is a            threatening driver. Advanced warning allows for evasive            own-car proactive re-planning.    -   “McDonald's stop” (spontaneous detouring)—rapid lateral change        (swaying) in direction and speed (azimuthal velocity vector)        -   Detection: Rapid change in radially projected length of            vehicle, and/or difference in velocity of rear and front of            vehicle [if range resolved] or change in azimuthal centroid            when SNR dependent range resolution is insufficient for the            above detection modalities.        -   Utility: Avoid rear-ending said detourer.            Additional behavior mode detection that involves various            mixtures of the above detection modes include:    -   Merging/Lane changing/passing—subtle or rapid lateral change in        direction coupled with subtle or rapid acceleration.    -   Emergency braking—z axis (radial) deceleration    -   Left turn initiation—radial deceleration in advance of a turn        lane.    -   Yellow light protection (jumping the yellow)—lateral        deceleration    -   Brake failure induced coasting (hill)—gradual acceleration at        consistent vector (with no deceleration).

While the invention has been described above in relation to its exampleembodiments, various modifications may be made thereto that still fallwithin the invention's scope. Such modifications to the invention willbe recognizable upon review of the teachings herein.

What is claimed is:
 1. An apparatus comprising: a ladar transmitterconfigured to transmit a cluster of ladar pulses within a ladar frametoward a target within a field of view, wherein each of a plurality ofthe ladar pulses in the cluster is temporally and spatially spaced apartrelative to the other ladar pulses in the cluster but spatiallyoverlapping with at least one of the other ladar pulses in the clusterat a specified distance in the field of view; a ladar receiverconfigured to receive reflections of the transmitted cluster of ladarpulses; and a circuit configured to (1) process data representative ofthe received reflections and (2) compute intra-frame motion data for thetarget based on the processed data.
 2. The apparatus of claim 1 whereinthe intra-frame motion data comprises intra-frame velocity data for thetarget.
 3. The apparatus of any claim 1 wherein the intra-frame motiondata comprises intra-frame acceleration data for the target.
 4. Theapparatus of claim 1 wherein the ladar transmitter comprises: aplurality of scanable mirrors; and a beam scanner controller configuredto aim the ladar transmitter toward the target via control over thescanable mirrors.
 5. The apparatus of claim 4 wherein the circuitcomprises a processor, the processor configured to detect the targetbased on at least one of ladar data and/or image data.
 6. The apparatusof claim 5 wherein the processor is further configured to: determine afirst coordinate along a first axis and a second coordinate along asecond axis in the field of view for the detected target, wherein thefirst and second axes are orthogonal to each other; define a first ladarpulse and a second ladar pulse for the cluster, wherein the first andsecond ladar pulses are both targeted to the first coordinate along thefirst axis but to different coordinates along the second axis such thatthe first and second ladar pulses are overlapping with each other at thespecified distance in the field of view; and process reflection data forthe first and second ladar pulses to compute a position for the detectedtarget along the second axis.
 7. The apparatus of claim 6 wherein theprocessor is further configured to: define a third ladar pulse for thecluster, wherein the third ladar pulse is targeted to the firstcoordinate along the first axis and the computed position along thesecond axis; and define a fourth ladar pulse for the cluster, whereinthe fourth ladar pulse is targeted to a third coordinate along the firstaxis and the computed position along the second axis, wherein the firstand third coordinates along the first axis are different but where atleast one of the third and fourth ladar pulses encompasses the detectedtarget; and process reflection data for the third and fourth ladarpulses to compute the intra-frame motion data for the detected target.8. The apparatus of claim 7 wherein the processor is configured to:compute range and intensity data for the reflections of the third andfourth ladar pulses based on the processed reflection data; compute across-range and range centroid of the detected target based on thecomputed range and intensity data; and compute the intra-frame motiondata for the detected target based on changes over time in the computedcentroids.
 9. The apparatus of claim 6 wherein the first axis isazimuth, and wherein the second axis is elevation.
 10. The apparatus ofclaim 4 wherein the ladar transmitter is further configured toselectively target range points in the field of view based oncompressive sensing.
 11. The apparatus of claim 1 wherein the circuit isfurther configured to: controllably select of plurality of ladar pulsesfor transmission during a subsequent frame based on the computedintra-frame motion data for the target.
 12. The apparatus of claim 1further comprising: a camera that is co-bore sited with the ladarreceiver, the camera configured to generate image data corresponding toa field of view for the ladar receiver; and wherein the circuitcomprises a processor, the processor configured to detect the targetbased on the generated image data.
 13. The apparatus of claim 1 whereinthe ladar pulses in the cluster have beam profiles that spatiallyoverlap at an offset with respect to a beam profile of at least one ofthe other ladar pulses in the cluster at the specified distance in thefield of view.
 14. The apparatus of any claim 2 wherein the intra-framemotion data further comprises intra-frame acceleration data for thetarget.
 15. A method comprising: transmitting a cluster of ladar pulseswithin a ladar frame toward a target within a field of view, whereineach of a plurality of the ladar pulses in the cluster is temporally andspatially spaced apart relative to the other ladar pulses in the clusterbut spatially overlapping with at least one of the other ladar pulses inthe cluster at a specified distance in the field of view; receivingreflections of the transmitted cluster of ladar pulses; processing datarepresentative of the received reflections; and computing intra-framemotion data for the target based on the processed data.
 16. The methodof claim 15 wherein the intra-frame motion data comprises intra-framevelocity data for the target.
 17. The method of claim 15 wherein theintra-frame motion data comprises intra-frame acceleration data for thetarget.
 18. The method of claim 15 further comprising: scanning aplurality of mirrors; and wherein the transmitting step comprises aimingand transmitting the ladar pulses toward the target via the scanningmirrors.
 19. The method of claim 18 further comprising: a processordetecting the target based on at least one of ladar data and/or imagedata.
 20. The method of claim 19 further comprising: a processordetermining a first coordinate along a first axis and a secondcoordinate along a second axis in the field of view for the detectedtarget, wherein the first and second axes are orthogonal to each other;a processor defining a first ladar pulse and a second ladar pulse forthe cluster, wherein the first and second ladar pulses are both targetedto the first coordinate along the first axis but to differentcoordinates along the second axis such that the first and second ladarpulses are overlapping with each other at the specified distance in thefield of view; and a processor processing reflection data for the firstand second ladar pulses to compute a position for the detected targetalong the second axis.
 21. The method of claim 20 further comprising: aprocessor defining a third ladar pulse for the cluster, wherein thethird ladar pulse is targeted to the first coordinate along the firstaxis and the computed position along the second axis; and a processordefining a fourth ladar pulse for the cluster, wherein the fourth ladarpulse is targeted to a third coordinate along the first axis and thecomputed position along the second axis, wherein the first and thirdcoordinates along the first axis are different but where at least one ofthe third and fourth ladar pulses encompasses the detected target; and aprocessor processing reflection data for the third and fourth ladarpulses to compute the intra-frame motion data for the detected target.22. The method of claim 21 further comprising: a processor computingrange and intensity data for the reflections of the third and fourthladar pulses based on the processed reflection data; a processorcomputing a cross-range and range centroid of the detected target basedon the computed range and intensity data; and a processor computing theintra-frame motion data for the detected target based on changes overtime in the computed centroids.
 23. The method of claim 20 wherein thefirst axis is azimuth, and wherein the second axis is elevation.
 24. Themethod of claim 15 further comprising: controllably selecting ofplurality of ladar pulses for transmission during a subsequent framebased on the computed intra-frame motion data for the target.
 25. Themethod of claim 15 further comprising: a camera that is co-bore sitedwith the ladar receiver generating image data corresponding to a fieldof view for the ladar receiver; and detecting the target based on thegenerated image data.
 26. The method of claim 15 further comprising:selectively targeting a plurality of the ladar pulses toward a pluralityof range points in the field of view based on compressive sensing. 27.The method of claim 15 wherein the ladar pulses in the cluster have beamprofiles that spatially overlap at an offset with respect to a beamprofile of at least one of the other ladar pulses in the cluster at thespecified distance in the field of view.
 28. The method of claim 16wherein the intra-frame motion data further comprises intra-frameacceleration data for the target.